Deep-Distributed-Learning-Based POI Recommendation Under Mobile-Edge Networks

Self Cite

The whole process of tobacco production is composed of many components, in which their operation and administration are currently independent. It is required to deploy smart manufacturing workflow for the whole production process, in order to realize centralized effective global scheduling. This requires an advanced administration control platform that has strong abilities of multisource data integration and automatic decision support. To bridge such research gap, this paper designs an optimal scheduling control system for smart manufacturing processes of tobacco industry. First of all, this work discusses major characteristics of future-generation production control patterns in intelligent tobacco factories (ITF). Then, a five-layer architecture for optimal scheduling control of ITF is proposed, which contains Internetof-Things layer, centralized control layer, model layer, platform layer and operation layer. In addition, a production scheduling optimization strategy is also developed for the proposed system to serve as the software algorithm that drives the running of whole smart manufacturing processes. Finally, this paper presents a comparative analysis of the proposed system's transformation in a cigarette factory. Naturally, the effectiveness of the proposed production optimization scheduling strategy is verified through simulation.

Section: Introductionmentioning

confidence: 99%

Design of an Optimal Scheduling Control System for Smart Manufacturing Processes in Tobacco Industry

Liu

Wang

et al. 2023

Self Cite

“…To ensure safety, like conventional software, testing techniques are often used to detect incorrect DNN behavior and improve DNN quality [6]. However, in automated testing of DNN-based systems, it is often not possible to directly define test predictions for the correct output given a given input [7]. To obtain the test prediction information, it is often necessary to spend expensive workforce to label the test data, which significantly slows down the quality assurance process [8].…”

Section: Introductionmentioning

confidence: 99%

Design of an Intelligent Educational Evaluation System Using Deep Learning

Yan

Lu²

2023

Nowadays, online education has been a more general demand in context of COVID-19 epidemic. The intelligent educational evaluation systems assisted by intelligent techniques are in urgent demand. To deal with this issue, this paper introduces the strong information processing ability of deep learning, and proposes the design of an intelligent educational evaluation system using deep learning. Inside the algorithm part, the low-complexity offset minimal sum (OMS) is selected as the front-end processor of deep neural network, so as to reduce following computational complexity in deep neural network. And the deep neural network is adopted as the major calculation backbone. In this paper, our OMS deep neural network parameters are 23 and 57 compared with other parameters, which can save about 59.64% of the network parameters, and the training time is 11270 s and 25000 s respectively, which saves the training time 54.92%. It can be also reflected from experiments that the proposal further improves the performance of unbalanced data classification in this problem scenario.

“…Especially in era of big data, the increasing data volume has brought more challenges to human expertise. In contemporary world, it remains a promising idea to utilize artificial intelligence algorithms to realize smart auditing affairs [6].…”

Section: Introductionmentioning

confidence: 99%

A Big Data-Driven Financial Auditing Method Using Convolution Neural Network

Zhao¹,

Wang

2023

In the big data era, traditional auditing methods are facing challenge such as limited audit scope, uneven distribution of audit power, and insufficient audit analysis. To pursue high efficiency, the utilization of big data analysis technique in financial auditing has been a novel tendency in this area. The deep learning has been popular in many areas due to its high freedom degree. Thus, this paper employs a typical deep learning model convolution neural network (CNN), and proposes a big data-driven financial auditing method using CNN. Specifically, the strong ability of feature abstraction of CNN is leveraged to extract multi-level features in materials, such as visual features, textual features, etc. Then, the multi-source features from auditing materials can be well fused for final discrimination. Some simulation experiments are conducted on real-world financial auditing scenes for assessment. And the results show that the designed the proposed financial auditing method possesses relatively high auditing accuracy.